Real-Time Measurement And System Monitoring Based On Generated Dependency Graph Models Of System Components

Abstract

Machine data is collected from multiple sources of an operating environment such as an information technology system, factory floor, or the like, into a data intake and query system, in one embodiment. Metrics representative of the environment are included in or derived from the data. Users may interact with an interface to depict a representation of various metrics and interdependencies and that depiction is reflected in a computer storage model. Changes to the computer storage model based on the user interaction may also result in training of a machine learning model according to the user interaction, the machine learning model configured to determine a prediction, classification or clustering of a result of a first search query by utilizing a result of at least a second search query as input to the machine learning model.

Description

FIELD[0001]Embodiments of the disclosure relate to generating a dependency graph based on a plurality of monitored assets. More specifically, one embodiment of the disclosure relates to a computerized method for analyzing ingested data to generate a dependency graph based on the ingested data, establishing relationships between nodes of the dependency graph and utilizing machine learning techniques to predict future metrics for a measurement underlying one or more of the nodes.GENERAL BACKGROUND[0002]Information technology (IT) environments can include diverse types of data systems that store large amounts of diverse data types generated by numerous devices. For example, a big data ecosystem may include databases such as MySQL and Oracle databases, cloud computing services such as Amazon web services (AWS), and other data systems that store passively or actively generated data, including machine-generated data (“machine data”). The machine data can include performance data, diagnost...

AI Technical Summary

Benefits of technology

The patent text describes a method for analyzing data collected from various sources and using machine learning techniques to predict future metrics. The method involves ingesting data, segmenting it into blocks, and annotating each block with metadata. The metadata helps identify relevant configuration files and extraction rules for accurate searches. The system can also store and analyze data from external sources. The technical effects of the patent include improved data management and analysis, flexibility in data retrieval and analysis, and improved user experience.

Problems solved by technology

The large amount and diversity of data systems containing large amounts of structured, semi-structured, and unstructured data relevant to any search query can be massive, and continues to grow rapidly.

This technological evolution can give rise to various challenges in relation to managing, understanding and effectively utilizing the data.

Images